【文章內(nèi)容簡介】 動力學(xué)分析與結(jié)構(gòu)優(yōu)化提供理論依據(jù)?？刂撇糠质钦麄€(gè)物料抓取機(jī)械手系統(tǒng)設(shè)計(jì)關(guān)鍵和核心，它在結(jié)構(gòu)和功能上的劃分和實(shí)現(xiàn)直接關(guān)系到機(jī)器人系統(tǒng)的可靠性、實(shí)用性，也影響和制約機(jī)械手系統(tǒng)的研制成本和開發(fā)周期。在控制主機(jī)的選用上，采用結(jié)構(gòu)緊湊、擴(kuò)展功能強(qiáng)和可靠性高的PC工業(yè)控制計(jì)算機(jī)作為主機(jī)，配以PCL839卡主要承擔(dān)系統(tǒng)功能初始化、數(shù)據(jù)運(yùn)算與處理、步進(jìn)電機(jī)驅(qū)動以及故障診斷等功能。同時(shí)對PCL839卡的結(jié)構(gòu)特點(diǎn)、功能原理和其高定位功能等給與了分析。硬件是整個(gè)控制系統(tǒng)以及極限位置功能賴以存在的物質(zhì)基礎(chǔ)，軟件則是計(jì)算機(jī)控制系統(tǒng)的神經(jīng)中樞，軟件設(shè)計(jì)的目的是以最優(yōu)的方式將各部分功能有機(jī)的結(jié)合起來，使系統(tǒng)具有較高的運(yùn)行效率和較強(qiáng)的可靠性。在物料抓取機(jī)械手軟件的設(shè)計(jì)上，采用的是模塊化結(jié)構(gòu)，分為系統(tǒng)初始化模塊、數(shù)據(jù)處理模塊和故障狀態(tài)檢測與處理等幾部分。主控計(jì)算機(jī)和各控制單元之間全部由PCL839卡聯(lián)系，并且由該卡實(shí)現(xiàn)抗干擾等問題，減少外部信號對系統(tǒng)的影響。步進(jìn)電機(jī)的啟停頻率遠(yuǎn)遠(yuǎn)小于其最高運(yùn)行頻率，為了提高工作效率，需要步進(jìn)電機(jī)高速運(yùn)行并快速啟停時(shí)，必須考慮它的升，降速控制問題。電機(jī)的升降速控制可以歸結(jié)為以某種合理的力一式控制發(fā)送到步進(jìn)電機(jī)驅(qū)動器的脈沖頻率，這可由硬件實(shí)現(xiàn)，也可由軟件方法來實(shí)現(xiàn)。本文提出了一種算法簡單、易于實(shí)現(xiàn)、理論意義明確的步進(jìn)電機(jī)變速控制策略:定時(shí)器常量修改變速控制方案。該方法能使步進(jìn)電機(jī)加速度與其力矩——頻率曲線較好地?cái)M合，從而提高變速效率。而且它的計(jì)算量比線性加速度變速和基于指數(shù)規(guī)律加速度的變速控制小得多。通過實(shí)驗(yàn)證明了該方法的有效性。最后，對論文主要研究內(nèi)容和取得的技術(shù)成果進(jìn)行了總結(jié)，提出了存在的問題和不足，同時(shí)對機(jī)器人技術(shù)的發(fā)展和應(yīng)用進(jìn)行了展望。第三篇：機(jī)器人算法外文翻譯Improved Genetic Algorithm and Its Performance AnalysisAbstract: Although genetic algorithm has bee very famous with its global searching, parallel puting, better robustness, and not needing differential information during , it also has some demerits, such as slow convergence this paper, based on several general theorems, an improved genetic algorithm using variant chromosome length and probability of crossover and mutation is proposed, and its main idea is as follows : at the beginning of evolution, our solution with shorter length chromosome and higher probability of crossover and mutation。and at the vicinity of global optimum, with longer length chromosome and lower probability of crossover and , testing with some critical functions shows that our solution can improve the convergence speed of genetic algorithm significantly , its prehensive performance is better than that of the genetic algorithm which only reserves the best algorithm is an adaptive searching technique based on a selection and reproduction mechanism found in the natural evolution process, and it was pioneered by Holland in the has bee very famous with its global searching, parallel puting, better robustness, and not needing differential information during , it also has some demerits, such as poor local searching, premature converging, as well as slow convergence recent years, these problems have been this paper, an improved genetic algorithm with variant chromosome length and variant probability is with some critical functions shows that it can improve the convergence speed significantly, and its prehensive performance is better than that of the genetic algorithm which only reserves the best section 1, our new approach is optimization examples, in section 2, the efficiency of our algorithm is pared with the genetic algorithm which only reserves the best section 3 gives out the , some proofs of relative theorems are collected and presented in of the algorithm Some theorems Before proposing our approach, we give out some general theorems(seeappendix)as follows: Let us assume there is just one variable(multivariable can be divided into many sections, one section for one variable)x ∈ [ a, b ] , x ∈ R, and chromosome length with binary encoding is 1Minimal resolution of chromosome is s = ba 2l1Theorem 2Weight value of the ith bit of chromosome iswi = bai1(i = 1,2,…l)2l1Theorem 3Mathematical expectation Ec(x)of chromosome searching step with onepoint crossover is Ec(x)= baPc 2lwhere Pc is the probability of 4Mathematical expectation Em(x)of chromosome searching step with bit mutation is Em(x)=(ba)Pm Mechanism of algorithmDuring evolutionary process, we presume that value domains of variable are fixed, and the probability of crossover is a constant, so from Theorem 1 and 3, we know that the longer chromosome length is, the smaller searching step of chromosome, and the higher resolution。and vice , crossover probability is in direct proportion to searching Theorem 4, changing the length of chromosome does not affect searching step of mutation, while mutation probability is also in direct proportion to searching the beginning of evolution, shorter length chromosome(can be too shorter, otherwise it is harmful to population diversity)and higher probability of crossover and mutation increases searching step, which can carry out greater domain searching, and avoid falling into local at the vicinity of global optimum, longer length chromosome and lower probability of crossover and mutation will decrease searching step, and longer length chromosome also improves resolution of mutation, which avoid wandering near the global optimum, and speeds up algorithm, it should be pointed out that chromosome length changing keeps individual fitness unchanged, hence it does not affect select ion(with roulette wheel selection). Description of the algorithmOwing to basic genetic algorithm not converging on the global optimum, while the genetic algorithm which reserves the best individual at current generation can, our approach adopts this evolutionary process, we track cumulative average of individual average fitness up to current is written as 1X(t)= GG229。ft=1avg(t)where G is the current evolutionary generation, is individual average When the cumulative average fitness increases to k times(k 1, k ∈ R)of initial individual average fitness, we change chromosome length to m times(m is a positive integer)of itself , and reduce probability of crossover and mutation, which can improve individual resolution and reduce searching step, and speed up algorithm procedure is as follows:Step 1 Initialize population, and calculate individual average fitness and set change parameter equal to , Step 2 Based on reserving the best individual of current generation, carry out selection, regeneration, crossover and mutation, and calculate cumulative average of individual average fitness up to current generationfavg。favgStep 3 Iffavg0≥k and Flag equals 1, increase chromosome length to m times of itself, and reduce probability of crossover and mutation, and set Flag equal to 0。otherwise continue 4 If end condition is satisfied, stop。otherwise go to Step Test and analysisWe adopt the following two criti